Matemática aplicada que permite interactuar con un conjunto de datos, que facilita además las labores de recolección, resumen, comparación y generalización de resultados.
PoblaciĂłn
Muestra
Variable
Individuo
| CentralizaciĂłn | DispersiĂłn | PosiciĂłn | DistribuciĂłn |
|---|---|---|---|
| Media | DesviaciĂłn estándar | Cuartiles | AsimetrĂa |
| Mediana | Varianza | Deciles | Curtosis |
| Moda | Rango | Percentiles |
Análisis exploratorio de datos que proporciona herramientas estadĂstica que permiten la visualizaciĂłn y representaciĂłn objetiva de la informaciĂłn.
| Escala de medida | Frecuencias | Posición | Dispersión | Distribución | Gráficos |
|---|---|---|---|---|---|
| Normal | Si | Moda | No | No | Barras, pastel |
| Ordinal | Si | Moda | No | No | Barras, áreas, pastel |
| Cuantitativas | No | Media,Moda, Mediana | Si | Si | Histogramas, áreas, dispersión |
graphicswith(cars, scatter.smooth(speed, dist, pch = 18, col = "dodgerblue4",
lpars = list(col = "red3", lwd = 2)))
grid(col = "gray80")latticelibrary(lattice)
xyplot(dist ~ speed, data = cars, type = c("p", "smooth"), pch = 18,
grid = TRUE, col = "dodgerblue4", col.line = "red3", lwd = 2)ggplot2library(ggplot2)
library(dplyr)
cars %>%
ggplot(data = ., aes(x = speed, y = dist)) +
geom_point(pch = 18, size = 2, color = "dodgerblue4") +
geom_smooth(se = FALSE, color = "red3", method = "loess", span = 2/3) +
theme_light()ggvislibrary(ggvis)
cars %>%
ggvis(x = ~speed, y = ~dist) %>%
layer_points(shape := "diamond", fill = "dodgerblue4", stroke := "black") %>%
layer_smooths(stroke := "firebrick", span = 2/3) %>%
hide_legend("fill")plotlylibrary(plotly)
cars %>%
plot_ly(x = ~speed, y = ~dist, color = I("dodgerblue4"), stroke = I("black"),
symbol = I("diamond"), type = "scatter") %>%
add_lines(y = ~fitted(loess(dist ~ speed, span = 2/3)), color = I("red3")) %>%
layout(showlegend = FALSE)dygraphslibrary(dygraphs)
lungDeaths <- cbind(mdeaths, fdeaths)
dygraph(lungDeaths)tauchartsdevtools::install_github("hrbrmstr/taucharts")
library(taucharts)
CO2 %>%
tauchart( ) %>%
tau_point( "conc", c("Treatment","uptake"), "Plant" ) %>%
tau_tooltip( ) %>%
tau_trendline( )metricsgraphicslibrary(metricsgraphics)
mtcars %>%
mjs_plot(x=wt, y=mpg, width=600, height=500) %>%
mjs_point(least_squares=TRUE) %>%
mjs_labs(x="Weight of Car", y="Miles per Gallon")highcharterlibrary("highcharter")
data(diamonds, mpg, package = "ggplot2")
hchart(mpg, "scatter", hcaes(x = displ, y = hwy, group = class))echarts4rlibrary(echarts4r)
e1 <- iris %>%
group_by(Species) %>%
e_charts(
Sepal.Length,
elementId = "chart"
) %>%
e_scatter(Petal.Length, Sepal.Width) %>%
e_datazoom(show = FALSE, y_index = 0) %>%
e_x_axis(min = 4)
e1